scholarly journals Robust Tensor Decomposition based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Rakesh R Kamath ◽  
hahn choo ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Moving Objects ◽  
Tensor Decomposition ◽  
Real Data ◽  
Video Data ◽  
Low Rank ◽  
Total Variation Regularization ◽  
L1 Norm ◽  
Melt Pool ◽  
Static Background

Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse Robust Tensor Decomposition (SS-RTD) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatiotemporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the L1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.<br>

scholarly journals Robust Tensor PCA based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Rakesh R Kamath ◽  
hahn choo ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Moving Objects ◽  
Real Data ◽  
Video Data ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Melt Pool ◽  
Alternating Direction ◽  
Spatio Temporal ◽  
Static Background

<div>Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse RTPCA (SS-RTPCA) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatio-temporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the `1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.</div>

scholarly journals Robust Tensor PCA based Background/Foreground Separation in Noisy Videos and Its Applications in Additive Manufacturing

2021 ◽  
Author(s):  
Bo Shen ◽  
Rakesh R Kamath ◽  
hahn choo ◽  
Zhenyu Kong
Keyword(s):  
Additive Manufacturing ◽  
Moving Objects ◽  
Real Data ◽  
Video Data ◽  
Low Rank ◽  
Total Variation Regularization ◽  
Melt Pool ◽  
Alternating Direction ◽  
Spatio Temporal ◽  
Static Background

<div>Background/foreground separation is one of the most fundamental tasks in computer vision, especially for video data. Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the video data is contaminated with noise. For example, in metal additive manufacturing (AM), the processed X-ray video to study melt pool dynamics is very noisy. RPCA and RTPCA are not able to separate the background, foreground, and noise simultaneously. As a result, the noise will contaminate the background or the foreground or both. There is a need to remove the noise from the background and foreground. To achieve the three terms decomposition, a smooth sparse RTPCA (SS-RTPCA) model is proposed to decompose the data into static background, smooth foreground, and noise, respectively. Specifically, the static background is modeled by the low-rank tucker decomposition, the smooth foreground (moving objects) is modeled by the spatio-temporal continuity, which is enforced by the total variation regularization, and the noise is modeled by the sparsity, which is enforced by the `1 norm. An efficient algorithm based on alternating direction method of multipliers (ADMM) is implemented to solve the proposed model. Extensive experiments on both simulated and real data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation in noisy cases.</div>

scholarly journals Robust Barron-Loss Tucker Tensor Decomposition

2021 ◽  
Author(s):  
Mahsa Mozaffari ◽  
Panos P. Markopoulos
Keyword(s):  
Loss Function ◽  
Tensor Decomposition ◽  
Real Data ◽  
Loss Functions ◽  
Low Rank ◽  
Numerical Studies ◽  
Rank Tensor ◽  
Algorithmic Solution ◽  
Tensor Approximation ◽  
New Formulation

<p>In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier resistance. The robustness of the proposed framework is corroborated by the presented numerical studies on synthetic and real data.</p>

scholarly journals Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction

2020 ◽  
Vol 34 (04) ◽  
pp. 4804-4810
Author(s):  
Ziyue Li ◽  
Nurettin Dorukhan Sergin ◽  
Hao Yan ◽  
Chen Zhang ◽  
Fugee Tsung
Keyword(s):  
Tensor Decomposition ◽  
Graph Laplacian ◽  
Efficient Estimation ◽  
Low Rank ◽  
Tensor Completion ◽  
L1 Norm ◽  
Passenger Flow ◽  
Rank Tensor ◽  
Improved Performance ◽  
Weakly Dependent Data

Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the L1-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate an improved performance over the regular tensor completion methods.

scholarly journals Robust Barron-Loss Tucker Tensor Decomposition

2021 ◽  
Author(s):  
Mahsa Mozaffari ◽  
Panos P. Markopoulos
Keyword(s):  
Loss Function ◽  
Tensor Decomposition ◽  
Real Data ◽  
Loss Functions ◽  
Low Rank ◽  
Numerical Studies ◽  
Rank Tensor ◽  
Algorithmic Solution ◽  
Tensor Approximation ◽  
New Formulation

<p>In this work, we propose a new formulation for low-rank tensor approximation, with tunable outlier-robustness, and present a unified algorithmic solution framework. This formulation relies on a new generalized robust loss function (Barron loss), which encompasses several well-known loss-functions with variable outlier resistance. The robustness of the proposed framework is corroborated by the presented numerical studies on synthetic and real data.</p>

scholarly journals Hierarchical low-rank and sparse tensor micro defects decomposition by electromagnetic thermography imaging system

2020 ◽  
Vol 378 (2182) ◽  
pp. 20190584
Author(s):  
Tongle Wu ◽  
Bin Gao ◽  
Wai Lok Woo
Keyword(s):  
Video Processing ◽  
Imaging System ◽  
Evaluation Criteria ◽  
Tensor Decomposition ◽  
Video Data ◽  
Low Rank ◽  
Destructive Testing ◽  
Imaging Device ◽  
Testing Field ◽  
Non Destructive

With the advancement of electromagnetic induction thermography and imaging technology in non-destructive testing field, this system has significantly benefitted modern industries in fast and contactless defects detection. However, due to the limitations of front-end hardware experimental equipment and the complicated test pieces, these have brought forth new challenges to the detection process. Making use of the spatio-temporal video data captured by the thermal imaging device and linking it with advanced video processing algorithm to defects detection has become a necessary alternative way to solve these detection challenges. The extremely weak and sparse defect signal is buried in complex background with the presence of strong noise in the real experimental scene has prevented progress to be made in defects detection. In this paper, we propose a novel hierarchical low-rank and sparse tensor decomposition method to mine anomalous patterns in the induction thermography stream for defects detection. The proposed algorithm offers advantages not only in suppressing the interference of strong background and sharpens the visual features of defects, but also overcoming the problems of over- and under-sparseness suffered by similar state-of-the-art algorithms. Real-time natural defect detection experiments have been conducted to verify that the proposed algorithm is more efficient and accurate than existing algorithms in terms of visual presentations and evaluation criteria. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

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